Drug response variability and optimal dosing in immuno-oncology
Roche Pharma Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche, Basel, Switzerland.
Objectives: Present some aspect of the biology and mechanistic PK/PD modelling, including clear applications in the early clinical space driving dose, regimen and possibly combination selection
Overview/Description of presentation: Leveraging, through quantitative methods, the time-course of tumour size and biomarker in oncology has been shown to be a relevant approach to avoid unnecessary toxicity, improve efficiency of active drugs, thus enabling an optimization of resource spending in patient care.
Immuno-oncology (IO) is becoming established as one of the main areas of focus for drug development. Early clinical findings indicate that a patient’s response to IO mostly depends on their immune system functions. For instance, expression of programmed-death ligand 1 (PD-L1) in tumour tissue appears to correlate positively to melanoma patients’ clinical response to nivolumab; and the inter-patient variability in immune function is viewed as a major factor to explain the differences in the timing of tumour response to ipilimumab. In addition, results have indicated that the immune factors associated with clinical response to ipilimumab and nivolumab in monotherapy might not translate to combinations. It is thus plausible that “fishing” for markers of efficacy, without being guided by a comprehensive and quantitative view of the immune system, is not the most efficient approach to successful IO drug development.
Developing computational models of the immune system in the context of IO can be viewed as a valuable tool to better explore the role of disease heterogeneity and to improve the identification of responders and the design of clinical trials. From a computational model of the immune system, many different treatment options and schedules can be simulated and the timing of biomarker sampling can be optimized to enhance the values of the experimentation.
Conclusions/Take home message: While leveraging early clinical data through the modelling the immune system represents a technological-demanding shift from the traditional way of analysing early clinical data, it can also represent a valid opportunity; for which each decision maker should pay attention, to assess the potential value to better support the clinical development of IO drugs.